Protein Structure Similarity Research Articles

Deep generative models of protein structure uncover distant relationships across a continuous fold space

Our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and understand protein structure, function and evolution. For instance, is there an optimal granularity in viewing protein structural similarities (e.g., architecture, topology or some other level)? Similarly, the discrete/continuous dichotomy of fold space is central, but remains unresolved. Discrete views of fold space bin similar folds into distinct, non-overlapping groups; unfortunately, such binning can miss remote relationships. While hierarchical systems like CATH are indispensable resources, less heuristic and more conceptually flexible approaches could enable more nuanced explorations of fold space. Building upon an Urfold model of protein structure, here we present a deep generative modeling framework, termed DeepUrfold, for analyzing protein relationships at scale. DeepUrfold’s learned embeddings occupy high-dimensional latent spaces that can be distilled for a given protein in terms of an amalgamated representation uniting sequence, structure and biophysical properties. This approach is structure-guided, versus being purely structure-based, and DeepUrfold learns representations that, in a sense, define superfamilies. Deploying DeepUrfold with CATH reveals evolutionarily-remote relationships that evade existing methodologies, and suggests a mostly-continuous view of fold space—a view that extends beyond simple geometric similarity, towards the realm of integrated sequence ↔ structure ↔ function properties.

Melodia: A Python Library for Protein Structure Analysis.

Analysing protein structure similarities is an important step in protein engineering and drug discovery. Methodologies that are more advanced than simple RMSD are available but often require extensive mathematical or computational knowledge for implementation. Grouping and optimising such tools in an efficient open-source library increases accessibility and encourages the adoption of more advanced metrics. Melodia is a Python library with a complete set of components devised for describing, comparing and analysing the shape of protein structures using differential geometry of three-dimensional curves and knot theory. It can generate robust geometric descriptors for thousands of shapes in just a few minutes. Those descriptors are more sensitive to structural feature variation than RMSD deviation. Melodia also incorporates sequence structural annotation and three-dimensional visualisations. Melodia is an open-source Python library freely available on https://github.com/rwmontalvao/Melodia_py, along with interactive Jupyter Notebook tutorials. Supplementary data are available at Bioinformatics online.

PPI3D: a web server for searching, analyzing and modeling protein-protein, protein-peptide and protein-nucleic acid interactions.

Structure-resolved protein interactions with other proteins, peptides and nucleic acids are key for understanding molecular mechanisms. The PPI3D web server enables researchers to query preprocessed and clustered structural data, analyze the results and make homology-based inferences for protein interactions. PPI3D offers three interaction exploration modes: (i) all interactions for proteins homologous to the query, (ii) interactions between two proteins or their homologsand (iii) interactions within a specific PDB entry. The server allows interactive analysis of the identified interactions in both summarized and detailed manner. This includes protein annotations, structures, the interface residues and the corresponding contact surface areas. In addition, users can make inferences about residues at the interaction interface for the query protein(s) from the sequence alignments and homology models. The weekly updated PPI3D database includes all the interaction interfaces and binding sites from PDB, clustered based on both protein sequence and structural similarity, yielding non-redundant datasets without loss of alternative interaction modes. Consequently, the PPI3D users avoid being flooded with redundant information, a typical situation for intensely studied proteins. Furthermore, PPI3D provides a possibility to download user-defined sets of interaction interfaces and analyze them locally. The PPI3D web server is available at https://bioinformatics.lt/ppi3d.

A Non-Active-Site Inhibitor with Selectivity for Zika Virus NS2B-NS3 Protease.

Flavivirus infection usually results in fever accompanied by headache, arthralgia, and, in some cases, rash. Although the symptoms are mild, full recovery can take several months. Flaviviruses encode seven nonstructural proteins that represent potential drug targets for this viral family. Focusing on the Zika virus NS2B-NS3 protease, we uncovered a unique inhibitor, MH1, composed of aminothiazolopyridine and benzofuran moieties. MH1 inhibits ZVP with a biochemical IC50 of 440 nM and effectively blocks cleavage of ZVP substrates in cells. Surprisingly, MH1 inhibits the other flaviviral proteases at least 18-fold more weakly. This same phenomenon was observed in assays of the viral cytopathic effect, where only Zika virus showed sensitivity to MH1. This selectivity was unexpected since flaviviral proteases have high similarity in sequence and protein structure. MH1 binds at an allosteric site, as demonstrated by its ability to stabilize ZVP synergistically with an active site inhibitor. To understand its selectivity, we constructed a series of hybrid proteases composed of select segments of ZVP, which is sensitive to MH1, and dengue virus protease, which is essentially insensitive to MH1. Our results suggest that MH1 binds to the NS3 protease domain, disrupting its interaction with NS2B. These interactions are essential for substrate binding and cleavage. In particular, the unique dynamic properties of NS2B from Zika seem to be required for the function of MH1. Insights into the mechanism of MH1 function will aid us in developing non-active-site-directed, pan-flaviviral inhibitors, by highlighting the importance of evaluating and considering the dynamics of the NS2B regions.

Structure-guided discovery of anti-CRISPR and anti-phage defense proteins

Bacteria use a variety of defense systems to protect themselves from phage infection. In turn, phages have evolved diverse counter-defense measures to overcome host defenses. Here, we use protein structural similarity and gene co-occurrence analyses to screen >66 million viral protein sequences and >330,000 metagenome-assembled genomes for the identification of anti-phage and counter-defense systems. We predict structures for ~300,000 proteins and perform large-scale, pairwise comparison to known anti-CRISPR (Acr) and anti-phage proteins to identify structural homologs that otherwise may not be uncovered using primary sequence search. This way, we identify a Bacteroidota phage Acr protein that inhibits Cas12a, and an Akkermansia muciniphila anti-phage defense protein, termed BxaP. Gene bxaP is found in loci encoding Bacteriophage Exclusion (BREX) and restriction-modification defense systems, but confers immunity independently. Our work highlights the advantage of combining protein structural features and gene co-localization information in studying host-phage interactions.

Fourier transform-based protein fraction analysis of whole-seed wheat

Abstract The principle of the Fourier transform is explored in this paper to compare protein structure similarity, transforming protein structure into a distance sequence, and performing spectral analysis on a fast Fourier transform. Secondly, protein fraction classification and similarity analysis of whole-seed wheat protein fractions were performed using the fast Fourier transform. Fourier transform infrared spectra were analyzed using two parameters: diatomic vibrations and molecular leaps. Finally, the whole seed wheat protein fraction content test analysis was analyzed experimentally. The results showed that the spectral range of whole seed wheat protein fractions was selected from 10539 cm −1 to 6080 cm −1, the fraction of fractions was determined as 9, the parameter R 2 val was 0.9614. This paper provides practical reference material for researching whole-seed wheat protein fields.

PC_ali: A tool for improved multiple alignments and evolutionary inference based on a hybrid protein sequence and structure similarity score.

Evolutionary inference depends crucially on the quality of multiple sequence alignments (MSA), which is problematic for distantly related proteins. Since protein structure is more conserved than sequence, it seems natural to use structure alignments for distant homologs. However, structure alignments may not be suitable for inferring evolutionary relationships. Here we examined four protein similarity measures that depend on sequence and structure (fraction of aligned residues, sequence identity, fraction of superimposed residues and contact overlap), finding that they are intimately correlated but none of them provides a complete and unbiased picture of conservation in proteins. Therefore, we propose the new hybrid protein sequence and structure similarity score PC_sim based on their main Principal Component. The corresponding divergence measure PC_div shows the strongest correlation with divergences obtained from individual similarities, suggesting that it infers accurate evolutionary divergences. We developed the program PC_ali that constructs protein MSAs either de novo or modifying an input MSA, using a similarity matrix based on PC_sim. tructs a starting MSA based on the maximal cliques of the graph of these PAs and it refines it through progressive alignments along the tree reconstructed with PC_div. Compared with eight state-of-the-art multiple structure or sequence alignment tools, PC_ali achieve higher or equal aligned fraction and structural scores, sequence identity higher than structure aligners although lower than sequence aligners, highest score PC_sim and highest similarity with the MSAs produced by other tools and with the reference MSA Balibase. https://github.com/ugobas/PC_ali. Supplementary data are available at Bioinformatics online.

Empowering drug off-target discovery with metabolic and structural analysis

Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Heterologous expression and characterization of Aquabacterium parvum lipase, a close relative of Ideonella sakaiensis PETase in Escherichia coli

Polyethylene terephthalate (PET) is a globally mismanaged plastic waste. Microbial degradation of plastics is a green and sustainable technology and very few studies report biocatalysts with plastic degradation potential. The present study aims to investigate the PETase from the evolution of IsPETase. Through NCBI database scanning, Aquabacterium parvum lipase (ApLip) was identified as an evolutionary intermediate between conventional lipases and IsPETase based on 16 S rRNA, amino acid sequences and protein structural similarity by simulation. The codon adaptation index (CAI) was used for ApLip to obtain ApLip-Ec and its protein expression was enhanced in Escherichia coli Lemo21(DE3) with a high catalytic efficiency of 11341 mM−1 min−1 based on p-nitrophenyl butyrate (pNPB) as a substrate. In vitro testing for PET degradation using 5.5 µM ApLip-Ec released 5.9 µM BHET after 48 h. Moreover, the synergism effect of ApLip-Ec and MHETase was explored. Successful PET degradation not only existed in Ideonella sakaiensis, but also evolved from structurally similar cutinases and lipases in nature.

Understanding the molecular mosaic of cardiotoxicity of light chains in plasma cell dyscrasias and cardiac light chain amyloidosis with the use of patient derived full-length light chains

Abstract Background Cardiac light chain amyloidosis (AL-CA) is a life-threatening disease and the major determinant of prognosis in AL amyloidosis. The management of heart failure (HF) in AL is challenging and gold standard therapies for HF are poorly tolerated or ineffective. Cardiac toxicity of LCs in AL-CA is poorly understood and the comparison of cardiotoxicity of LCs derived from plasma cell dyscrasias (PCDs) such as multiple myeloma (MM) and monoclonal gammopathy of undetermined significance (MGUS), will improve our understanding of the mechanisms of cardiac damage. Purpose We aimed to 1) genetically identify and biotechnologically produce full-length LCs from patients with AL-CA, MM, MGUS or non-clonal LCs from healthy volunteers (HV), 2) identify LCs' cardiotoxicity and 3) investigate the underlying mechanisms of cardiotoxicity in vitro. Methods Bone marrow derived CD138+ cells from n=7 patients with AL-CA, n=2 patients with MM and n=2 patients with MGUS and peripheral blood mononuclear cells (PBMCs) from n=2 HV were isolated for RNA extraction and characterization of the LC gene family repertoire. At the protein level, LC expression was confirmed by immunoprecipitation in patients' serum followed by top-down proteomics. The overexpressed LC genes in each patient, encoding the full-length clonal LCs were cloned and produced in Shuffle E. coli cells. Two LCs were produced from HV based on the primary protein structure similarity with the patients' LCs. Primary adult ventricular murine cardiomyocytes (pAVMCs) were isolated and exposed at various LC concentrations for evaluation of cell death and investigation of the cardiotoxicity mechanisms via gene and protein expression. LCs folding, oligomerization and amyloidogenic potential were assessed via circular dichroism (CD), SDS page and electron microscopy respectively. Results We successfully identified the LCs responsible for the disease and isolated the respective proteins in all cases (7 AL-CA, 2 MM, 2 MGUS and 3 HV). Despite the similarity of the LCs in conformation as beta-sheet and oligomerization mainly as dimers, 5 out of 7 AL-CA derived LCs led to a different extent of cardiotoxicity in pAVMCs compared to the HV, MM and MGUS derived LCs which did not alter cell viability. Interestingly, these 5 LCs bared the highest amyloidogenic potency. LCs induced different molecular responses leading to cardiomyocyte death. AL-CA proteins κ-type induced apoptosis and overexpression of endoplasmic reticulum stress (ERS) markers while LCs λ-type increased unfolded protein response (UPR) markers and autophagy without inducing apoptosis. All LCs of κ-type including from MM and MGUS patients led to inteleukin-6 mediated inflammation indicating that this mechanism is independent of the observed toxicity. Conclusions AL-CA derived LCs induce cardiotoxicity, which correlates to their amyloidogenic potential via ERS, UPR, autophagy and apoptosis which can be considered targets for cardioprotection. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Hellenic Foundation for Research and Innovation

TMQuery: a database of precomputed template modeling scores for assessment of protein structural similarity.

Comparisons of protein structures are critical for developing novel protein designs, annotating protein functions and predicting protein structure. The template modeling score (TM-score) is a widely used but computationally expensive measure of protein similarity that is applicable to a wide variety of structural biology problems. We introduce TMQuery-a continuously updated database containing over eight billion pre-computed TM-score values for every pair of proteins in the Protein Data Bank, allowing researchers to quickly query and download TM-scores via a web interface. Publicly available at https://tmquery.gsk.com/.

Structural Insights for the Stronger Ability of Shrimp Ferritin to Coordinate with Heavy Metal Ions as Compared to Human H-Chain Ferritin.

Although apoferritin has been widely utilized as a new class of natural protein nanovehicles for encapsulation and delivery of nutraceuticals, its ability to remove metal heavy ions has yet to be explored. In this study, for the first time, we demonstrated that the ferritin from kuruma prawns (Marsupenaeus japonicus), named MjF, has a pronouncedly larger ability to resist denaturation induced by Cd2+ and Hg2+ as compared to its analogue, human H-chain ferritin (HuHF), despite the fact that these two proteins share a high similarity in protein structure. Treatment of HuHF with Cd2+ or Hg2+ at a metal ion/protein shell ratio of 100/1 resulted in marked protein aggregation, while the MjF solution was kept constantly clear upon treatment with Cd2+ and Hg2+ at different protein shell/metal ion ratios (50/1, 100/1, 250/1, 500/1, 1000/1, and 2500/1). Structural comparison analyses in conjunction with the newly solved crystal structure of the complex of MjF plus Cd2+ or Hg2+ revealed that cysteine (Cys) is a major residue responsible for such binding, and that the large difference in the ability to resist denaturation induced by these two heavy metal ions between MjF and HuHF is mainly derived from the different positions of Cys residues in these two proteins; namely, Cys residues in HuHF are located on the outer surface, while Cys residues from MjF are buried within the protein shell. All of these findings raise the high possibility that prawn ferritin, as a food-derived protein, could be developed into a novel bio-template to remove heavy metal ions from contaminated food systems.

Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research.

Since the outset of COVID-19, the pandemic has prompted immediate global efforts to sequence SARS-CoV-2, and over 450 000 complete genomes have been publicly deposited over the course of 12 months. Despite this, comparative nucleotide and amino acid sequence analyses often fall short in answering key questions in vaccine design. For example, the binding affinity between different ACE2 receptors and SARS-COV-2 spike protein cannot be fully explained by amino acid similarity at ACE2 contact sites because protein structure similarities are not fully reflected by amino acid sequence similarities. To comprehensively compare protein homology, secondary structure (SS) analysis is required. While protein structure is slow and difficult to obtain, SS predictions can be made rapidly, and a well-predicted SS structure may serve as a viable proxy to gain biological insight. Here we review algorithms and information used in predicting protein SS to highlight its potential application in pandemics research. We also showed examples of how SS predictions can be used to compare ACE2 proteins and to evaluate the zoonotic origins of viruses. As computational tools are much faster than wet-lab experiments, these applications can be important for research especially in times when quickly obtained biological insights can help in speeding up response to pandemics.

Structural similarity-based prediction of host factors associated with SARS-CoV-2 infection and pathogenesis

The current pandemic resulted from SARS-CoV-2 still remains as the major public health concern globally. The precise mechanism of viral pathogenesis is not fully understood, which remains a major hurdle for medical intervention. Here we generated an interactome profile of protein-protein interactions based on host and viral protein structural similarities information. Further computational biological study combined with Gene enrichment analysis predicted key enriched pathways associated with viral pathogenesis. The results show that axon guidance, membrane trafficking, vesicle-mediated transport, apoptosis, clathrin-mediated endocytosis, Vpu mediated degradation of CD4 T cell, and interferon-gamma signaling are key events associated in SARS-CoV-2 life cycle. Further, degree centrality analysis reveals that IRF1/9/7, TP53, and CASP3, UBA52, and UBC are vital proteins for IFN-γ-mediated signaling, apoptosis, and proteasomal degradation of CD4, respectively. We crafted chronological events of the virus life cycle. The SARS-CoV-2 enters through clathrin-mediated endocytosis, and the genome is trafficked to the early endosomes in a RAB5-dependent manner. It is predicted to replicate in a double-membrane vesicle (DMV) composed of the endoplasmic reticulum, autophagosome, and ERAD machinery. The SARS-CoV-2 down-regulates host translational machinery by interacting with protein kinase R, PKR-like endoplasmic reticulum kinase, and heme-regulated inhibitor and can phosphorylate eIF2a. The virion assembly occurs in the ER-Golgi intermediate compartment (ERGIC) organized by the spike and matrix protein. Collectively, we have established a spatial link between viral entry, RNA synthesis, assembly, pathogenesis, and their associated diverse host factors, those could pave the way for therapeutic intervention.

MODL-13. GENETICALLY ENGINEERED PIG MODEL OF RHABDOID TUMOR PREDISPOSITION SYNDROME-1

Atypical teratoid/rhabdoid tumor (AT/RT) is the most common malignant CNS tumor of children below 6 months of age. The majority of AT/RT demonstrate genomic alterations in the SMARCB1 gene. There are two major hurdles in the development of safe and effective treatments for AT/RT: first, the mouse models do not fully recapitulate the disease seen in patients and their predictivity of clinical efficacy is still unproven. Second, due to a small patient population, the ability to recruit enough patients for clinical trials is challenging. Genetic studies have demonstrated that germline deletion of SMARCB1 exons 4 and 5 predisposes to AT/RT at an early age. Comparison of human, swine, and mouse SMARCB1 genes show similarities in gene and protein structure, with 100% amino acid identity between swine and human SMARCB1 isoforms. Thus, we hypothesized that germline deletion of exons 4 and 5 will predispose heterozygote swine to AT/RT development. SMARCB1+/- founder pigs are obtained using a CRISPR/Cas9 mediated gene-editing of conventional crossbred swine embryos, followed by embryo transfer into female swine surrogates. They are evaluated for clinical criteria used to diagnose AT/RT and by MRI at 6, 12, and 24 months of age, followed by histopathology and molecular analysis of the tumors as they are detected. Generating a large animal model of AT/RT would represent a breakthrough in the field from a genomic, pathophysiologic, pre-clinical and therapeutic perspective.

A Sweep of Earth's Virome Reveals Host-Guided Viral Protein Structural Mimicry and Points to Determinants of Human Disease.

Viruses deploy genetically encoded strategies to coopt host machinery and support viral replicative cycles. Here, we use protein structure similarity to scan for molecular mimicry, manifested by structural similarity between viral and endogenous host proteins, across thousands of cataloged viruses and hosts spanning broad ecological niches and taxonomic range, including bacteria, plants and fungi, invertebrates, and vertebrates. This survey identified over 6,000,000 instances of structural mimicry; more than 70% of viral mimics cannot be discerned through protein sequence alone. We demonstrate that the manner and degree to which viruses exploit molecular mimicry varies by genome size and nucleic acid type and identify 158 human proteins that are mimicked by coronaviruses, providing clues about cellular processes driving pathogenesis. Our observations point to molecular mimicry as a pervasive strategy employed by viruses and indicate that the protein structure space used by a given virus is dictated by the host proteome.A record of this paper’s transparent peer review process is included in the Supplemental Information.

Real time structural search of the Protein Data Bank.

Detection of protein structure similarity is a central challenge in structural bioinformatics. Comparisons are usually performed at the polypeptide chain level, however the functional form of a protein within the cell is often an oligomer. This fact, together with recent growth of oligomeric structures in the Protein Data Bank (PDB), demands more efficient approaches to oligomeric assembly alignment/retrieval. Traditional methods use atom level information, which can be complicated by the presence of topological permutations within a polypeptide chain and/or subunit rearrangements. These challenges can be overcome by comparing electron density volumes directly. But, brute force alignment of 3D data is a compute intensive search problem. We developed a 3D Zernike moment normalization procedure to orient electron density volumes and assess similarity with unprecedented speed. Similarity searching with this approach enables real-time retrieval of proteins/protein assemblies resembling a target, from PDB or user input, together with resulting alignments (http://shape.rcsb.org).

A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function.

Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how’ these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).

Genome Wide Identification of C2H2-Type Zinc Finger Proteins of Tomato and Expression Analysis Under Different Abiotic Stresses

In plants, C2H2-type zinc finger proteins play important roles in multiple processes, including plant growth and development, as well as biotic and abiotic responses. In the present study, based on the presence of the C2H2 domain (CX2~4CX3FX5LX2HX3~5H), 112 C2H2-type zinc finger proteins were predicted in tomato. Through gene and protein structures analyses and phylogenetic analysis, the 112 C2H2-type zinc finger proteins were divided into five subfamilies. Members of the same subfamily shared similarities in gene and protein structures, while members of different subfamilies contained different numbers of the C2H2 domain. The tissue expression pattern analysis showed that 24 C2H2-type zinc finger proteins are constitutively expressed in all tissues, indicating that they may play important roles in the growth and development of all tissues. In addition, under chilling (4 °C), heat (42 °C), high salinity (200 Mm NaCl), and osmotic (20% PEG) stresses, members of C2H2-type zinc finger family were induced to varying degrees, which suggested that these genes were involved in multiple abiotic stress responses. This study will provide theoretical basis for further research of C2H2-type zinc finger proteins in tomato.

MADOKA: an ultra-fast approach for large-scale protein structure similarity searching

BackgroundProtein comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years. However, facing the rapid growth of protein structure data, improving overall comparison performance and running efficiency with massive sequences is still challenging.ResultsHere, we propose MADOKA, an ultra-fast approach for massive structural neighbor searching using a novel two-phase algorithm. Initially, we apply a fast alignment between pairwise structures. Then, we employ a score to select pairs with more similarity to carry out a more accurate fragment-based residue-level alignment. MADOKA performs about 6–100 times faster than existing methods, including TM-align and SAL, in massive alignments. Moreover, the quality of structural alignment of MADOKA is better than the existing algorithms in terms of TM-score and number of aligned residues. We also develop a web server to search structural neighbors in PDB database (About 360,000 protein chains in total), as well as additional features such as 3D structure alignment visualization. The MADOKA web server is freely available at: http://madoka.denglab.org/ConclusionsMADOKA is an efficient approach to search for protein structure similarity. In addition, we provide a parallel implementation of MADOKA which exploits massive power of multi-core CPUs.